CN102693134A - Sensor network software modeling platform development method based on unified modeling language - Google Patents
Sensor network software modeling platform development method based on unified modeling language Download PDFInfo
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Abstract
The invention discloses a sensor network software modeling platform development method based on a unified modeling language and a genetic algorithm. By combining with the thought of an intelligent agent and the genetic algorithm, a model suitable for sensor network software development is built with the unified modeling language; the built model is optimized by the genetic algorithm; and a modeling platform based on the model is built. A model formed the unified modeling language based on the intelligent agent is built, and by defining a meta-model of a domain model formed by the unified modeling language based on the intelligent agent, the domain model comprises a plurality of diagrammatic figures, namely, an activity diagram of a group, an intelligent agent diagram owned by the group, a group diagram to which the intelligent agent belongs, a role diagram owned by the group and a relational graph between groups. The model is optimized by the genetic algorithm, so that attribute redundancy in the group in the model is lowered, and the model with the perfect performance is obtained. According to the sensor network software modeling platform development method, a purpose that a sensor network software model is visually built is realized, and the sensor network software development difficulty is lowered.
Description
Technical field
The present invention relates to a kind of in sensing net software modeling platform development process; The thought of combined with intelligent body and genetic algorithm; Use UML to make up the model that is fit to the software development of sensing net, the model of creating through genetic algorithm optimization again, and make up Modeling Platform based on this model.This technology belongs to the interleaving techniques field of computer network, artificial intelligence and sensing net.
Background technology
The sensing network technology is 21 centurys three one of big research technology.The radio sensing network technology is the dual-use strategic hi-tech that typically has cross discipline character, can be widely used in fields such as national defense and military, national security, environmental science, traffic administration, hazard prediction, health care, manufacturing industry, the development of urban informationization.The sensing net is made up of the identical or different wireless sensor node of various functions, and each sensor node is made up of data acquisition module (sensor, A/D converter), data processing and control module (microprocessor, storer), communication module (wireless transceiver) and supply module (battery, DC/AC energy converter) etc.The microminiaturization that develops into sensor of microelectron-mechanical process technology in the recent period provides possibility, and the development of microprocessing has promoted Intelligence of Sensors, has promoted the birth of wireless senser and network thereof through the fusion of technology for radio frequency.It is microminiaturized, intelligent, information-based, networked that traditional sensor is just progressively realized, just experiencing one from the traditional sensors to the intelligence sensor, the evolution of enriching constantly to the intension of embedded Web sensor again.
The realistic meaning of sensing net is with the internet to be that the computer networking technology of representative is a great achievement of twentieth century computer science, and it has brought deep variation for people's life, yet at present; Network function is powerful again, and network world is abundant again, also is virtual eventually; The real world that it and people are lived still is separated by, and in network world, is difficult to the perception real world; A lot of things still are impossible, and the epoch are being called new network technology.The brand-new network technology that sensing network arises at the historic moment under such background just; It combines sensor, low-power consumption, communication and micro electronmechanical or the like technology; Can predict, in the near future, sensing network will bring revolutionary variation to people's life style.
Sensing net Development of Software still is in the junior stage, although more perfect operating system has been arranged, development language is aimed at large-scale sensing net software development, still lacks a development platform of being convenient to design use.The meaning of this patent just is auxiliary sensing net Development of Software design.Intelligent body thought and UML are combined, utilize the new features of UML 2.0, the expansion UML makes it become the UML based on intelligent body of supporting intelligent body, assists sensing net Software Design.Utilize the explanation of UML; Visual with the establishment document characteristic; Can from the Frame Design of complicacy, free the programmer, make its realization that can be absorbed in software function, and let the designer can understand the architecture of software from a higher abstract angle; Be easy to the client and understand, make things convenient for the interchange to designing a model between the designer.
Though the soft project research to the exploitation of sensing net still is in the starting stage at present, relates to many aspects such as development approach, safety, route, viability, self-organization, architecture, maintenance, renewal.The model-driven exploitation is a kind of development approach very important in the software development methodology, has plurality of advantages, is well suited for sensing net Development of Software.One of subject matter of software development now is exactly: numerous items is eager very much to set about programming, and too many energy is placed on writes on the code.The a part of reason that causes this phenomenon is that many supvrs lack the understanding to software development process, so when their programming team does not produce code, just become very anxious.In addition, compare with the work of setting up system's abstract model, the programming personnel feels that programing work is more sure.And in many cases, for a long time, industry is weighed a programmer's level according to lines of code rather than solution, and this custom has also been made a lot of such problems.
UML (Unified Modeling Language) is used for the software key collecting system is carried out a kind of language of visual modeling.UML be the product of object-oriented development system describe, visual and the establishment document a kind of standard language.In UML each stage in the software development cycle, adopted as industrywide standard by OMG.UML is best suited for data modeling, business model, object modeling, assembly modeling.
The intelligence body may be defined as " one can be according to its perception to its environment, the software entity with behavior of initiatively taking to make a strategic decision ".The determinant attribute of intelligence body mainly contains independence, interactivity, adaptability, intelligent, concertedness, movability etc., and independence representes not have outside directly interference to take action according to self experience; Interactivity is represented to exchange with environment and other intelligent body; Adaptability is illustrated in can respond other intelligent body or environment in a way; Intelligent is to use synthetic language mutual by the knowledge formal state of institute and other intelligent body; Concertedness is meant the collaborative work in the multi-agent system environment of intelligent physical efficiency, to carry out and to accomplish the complex task that some are benefited each other; Movability is represented and can be transferred to another environment from an environment with own.In fact, be difficult to see that an intelligent body has above-mentioned various characteristics, it is generally acknowledged that first three items is essential.
Above by the agency of the content of UML, briefly be exactly UML based on the UML of intelligent body towards intelligent body.The research of multi-agent system is generally defined as the expansion of object-oriented system.Yet it is, usually awkward when people attempt using the Object-Oriented Design instrument to describe some distinguished characteristics of multi-agent system because this definition is too simple.Therefore the UML based on intelligent body arises at the historic moment.
Summary of the invention
Technical matters:The objective of the invention is to propose a kind of sensing net software modeling platform development method; Adopt UML combined with intelligent bulk properties to make up model; Utilize the genetic algorithm optimization model; The exploitation of Graphics Application model framework makes up sensing net software model based on the Modeling Platform of this model under this platform again.The present invention provides platform for high-efficient simple ground makes up the wireless sense network software model, has solved the deficiency of common software modeling instrument.
Technical scheme:Sensing net software modeling platform development method based on UML and genetic algorithm according to the invention is a kind of comprehensive method; At first make up the domain model contain a plurality of illustratons of model based on the UML of intelligent body; This domain model is to be based upon on the basis of intelligent body thought and wireless sense network characteristics; Through using intelligent body that the sensing net is carried out modeling, make the sensing net node software development modelization; Next utilizes genetic algorithm that the model that makes up is optimized the model that can obtain perfect performance, and optimizing process reduces in the model redundancy of attribute between the group, reduces the complexity of model, makes with the group to be that the field modeling of unit is more clear efficient; Utilize the graphical model framework to make up sensing net software modeling platform at last, realize the construction of sensing net software model, reduce the difficulty of sensing net software development, improve sensing net software development efficiency based on this model.
When sensing net software modeling platform development method of the present invention is to make up sensing software modeling platform off the net; Introduced the method that the UML model based on intelligent body combines with genetic algorithm; Mode with meta-model defines the domain model based on the UML model of intelligent body; Generate the UML model that is fit to the software development of facing sensing net based on intelligent body; This obtains the better model of performance through genetic algorithm optimization again, has gone out the software modeling platform through graphical model framework developing plug at last, and the step that described method comprises is:
Step 1) definition domain model: utilize intelligent body thought and sensing net characteristics; Define domain model figure with the meta-model mode based on the UML of intelligent body; This domain model figure is contained a plurality of illustratons of model; Graph of a relation between comprising role figure that intelligent body figure, picture group that intelligent body belongs to, group that group activity diagram, group are had are had and group and organizing
1.1) intelligent body class, intelligent body sensing net node class and intelligent body role class
Two seed categories are divided and defined to described intelligent body class definition several different methods: intelligent body sensing net node class and intelligent body role class; Intelligence body role class defines the various roles that intelligent body possibly played the part of, and the class definition of intelligent body sensing net node moves the fundamental characteristics of the sensing net node of intelligent body;
1.2) intelligent body entity and intelligent body class
Described intelligent body entity is to be an intelligent body instance based on intelligent system of intelligent body class and the definition of intelligent body by the most basic modeling structure unit; These instances are also directly carried out some important intelligent body instance, the public attribute of the relevant intelligent body of described intelligent body class definition by the bottom property sort of dividing;
1.3) group, intelligent body group and non intelligent body group
Described group is the set of a plurality of intelligent bodies that are associated through the role, and these are related forms one group also is made up of some related roles at the inner figure that is connected of group, and these roles have a plurality of intelligent body instances; Described group preplans a bit; Some is interim urgent foundation; Described intelligent body group is exactly that those itself just have the group of the attribute of intelligent body, has more strong functions, accomplishes more complicated task; Described non intelligent body group is the instance with intelligent bulk properties, is a set rather than intelligent body;
1.4) intelligent body sensing net node class and intelligent body role assignments related
The taproot attribute of the intelligent body that described intelligent body sensing net node class decision is related with it; Intelligence body class determines other attributes of intelligent body; Direct line decision between said intelligent body role class and the intelligent body should be played the part of certain role by the intelligence body; It is promptly inner a specific group that a said intelligent body and role related is based upon in the specific context environmental, and described intelligent body role assignments is entity mapping relations that are based upon between intelligent body, role and the group;
Step 2) optimizes domain model: utilize genetic algorithm that the model that makes up is optimized the model that can obtain perfect performance; The redundancy of attribute between the group in the optimizing process minimizing model; Reduce the complexity of model, make with the group to be that the field modeling of unit is more clear efficient;
Step 3) makes up sensing net software modeling platform: the structure of sensing net software modeling platform; At first set up meta-model domain model based on the UML of intelligent body; Utilize graphical model framework plug-in unit again, set up the domain model of sensing net by the first step; Second step generated the graphical definition model of sensing net domain model; The 3rd step generated the instrument definition model of sensing net domain model; The mapping model definition of the 4th step sensing net domain model; The step of the last model of generation of the 5th step sensing net domain model, the structure of completion platform.
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Beneficial effect:The inventive method has proposed a kind of sensing net software modeling platform development method based on UML and genetic algorithm; Through making up UML model based on intelligent body; And use the genetic algorithm optimization model; Make model optimum,, realize the construction of sensing net software model according to this model construction sensing net software modeling platform.Specifically, method of the present invention has following beneficial effect:
(1) sensing net node according to the invention has intelligent bulk properties, and the notion with sensing net node in modeling process is mapped as an intelligent body, and the analogy of sensing net software modeling is based on the software modeling of intelligent body.
(2) sensing net software modeling development platform according to the invention has reduced sensing net Development of Software cost significantly, reduces the difficulty of sensing net software development, has reduced developer's workload, has improved Development of Software efficient.
(3) the UML model based on intelligent body of facing sensing net according to the invention software development defines domain model with the meta-model mode.And the various piece of specific definition domain model makes it can contain a plurality of illustratons of model, has certain characteristics and advance.
(4) core concept of structure model according to the invention is combined with intelligent bulk properties and sensing net characteristics.The intelligence body very well reflects the characteristic of sensing net node, is fit to very much the modeling exploitation of sensing net node.
(5) utilize genetic algorithm, propose the fitness function that sensing net software model is optimized, Optimization Model has reduced in the model attribute redundance in the group, obtains the model of perfect performance.
Description of drawings
Fig. 1 is a graphical model framework development procedure synoptic diagram,
Fig. 2 is the domain model synoptic diagram of using based on the sensing net node of meta-model,
Fig. 3 is the synoptic diagram of intelligent body class, intelligent body role class instance and link,
Fig. 4 is intelligent body class, intelligent body instance and a type related synoptic diagram,
Fig. 5 is the synoptic diagram of group and intelligent body role class,
Fig. 6 is that intelligent body, group and role-map concern synoptic diagram.
Embodiment
One, architecture
Sensing net software modeling platform development method system structure based on UML and genetic algorithm of the present invention comprises: based on the UML model of intelligent body, Optimization Model, based on the sensing net software modeling platform of Optimization Model.
Described UML model based on intelligent body utilizes intelligent body thought and sensing net characteristics, defines the domain model based on the UML of intelligent body through the meta-model mode; This model is contained a plurality of illustratons of model; Comprise the group activity diagram, the intelligent body figure that group is had, the picture group that intelligent body belongs to; The role figure that group is had, the graph of a relation between group and the group.
Described genetic algorithm key step is following:
(1) generates the initial population with N individuals, population evolutionary generation gen=0 at random.
(2) confirm internodal connected relation matrix M according to each node transmitting-receiving radius.
(3) calculate chromosomal fitness function in the colony.
(4) population is evolved:
1) select operator: according to the fitness function that calculates in (3), selecting optimum preceding 3n (n < N/>3) individuals is male parent, is divided into the n group at random.
2) crossover operator: adopt heuristic operator; 3n male parent to choosing in
intersected in twos; As 3 male parents among the 3n
are carried out interlace operation, will produce 3 new offspring individuals
; 2) crossover operator: adopt heuristic operator; 3n male parent to choosing in
intersected in twos; As 3 male parents among the 3n
are carried out interlace operation, will produce 3 new offspring individuals
.
3) mutation operator:, adopt the method that exchanges 2 gene position at random to accomplish with probability
variation.
(5) as if evolutionary generation gen=MAXGEN, then algorithm stops; Otherwise gen=gen+1 changes (3).
Described sensing net software modeling platform is to be developed by the plug-in unit graphical model framework of IDE.The major function of graphical model framework editing machine is the model that one group of definition is used for the generating code framework, mainly comprises: graphical model, tool model, mapping model.Can carry out the exploitation of graphical model framework according to this guide, make performance history more clear and easy.Sensing net of the present invention modeling is to carry out the exploitation of graphical model framework by the step that figure one provides, and creates out a graphical modeling tool platform based on domain model.
Two, method flow
1, the meta-model figure of the whole domain model of definition
The software development of sensing net is to be domain model with the UML model based on intelligent body, in conjunction with the characteristics of sensing net, expands common UML model based on intelligent body.UML model based on intelligent body itself is the UML model that makes up towards multi-agent system, is the expansion to UML.Comprehensive multiple UML model modelling approach based on intelligent body is selected to define this domain model through the mode of meta-model.Be the definition of each part of meta-model of domain model below, figure two shows the meta-model figure of whole domain model.
(1) intelligent body class, intelligent body sensing net node class and intelligent body role class
Intelligence body class definition several different methods is used for dividing and defining two seed categories: intelligent body sensing net node class and intelligent body role class.The class definition of intelligence body sensing net node moves some intrinsic characteristics of the sensing net node of intelligent body.Intelligence body role class defines the various roles of the time dependent different qualities that intelligent body possibly play the part of.The intelligence body is solidificated on the sensing net node, and its intelligent body sensing net node class is changeless (removes intelligent mobile agent, support code move), and in an activity, the intelligent body role class of intelligent body be change and have different forms.
(2) intelligent body entity and intelligent body class
Intelligence body class and intelligent body are the most basic modeling structure unit of intelligent body, define one based on the intelligent body instance of intelligent system and with these instance classification.These intelligent body instances are directly carried out, through the bottom characteristic of classifying and dividing them.The public attribute of intelligence body class description intelligence body, these public attributes comprise free state, the service that provides.Intelligent body be one homemade, have surrounding environment reflexive, based on the entity in the intelligent system.
(3) group, intelligent body group and non intelligent body group
Described group is the set of a plurality of intelligent bodies that are associated through the role, and these are related forms one at the inner figure that is connected of group.Group also is made up of some related roles, and these roles have a plurality of intelligent body instances.Described group preplans a bit, and some is interim urgent foundation.Described intelligent body group is exactly that those itself just have the group of the attribute of intelligent body, has more strong functions, accomplishes more complicated task.Described non intelligent body group is the instance with intelligent bulk properties, is a set rather than intelligent body.
(4) intelligent body sensing net node class and intelligent body role assignments is related
The taproot attribute of the intelligent body that described intelligent body sensing net node class decision is related with it.Intelligence body class determines other attributes of intelligent body.Direct line decision between said intelligent body role class and the intelligent body should be played the part of certain role by the intelligence body.A said intelligent body is based upon in the specific context environmental promptly a specific group inside with the related of role.Described intelligent body role assignments is entity mapping relations that are based upon between intelligent body, role and the group.
, genetic algorithm optimizing process
Intelligent body of a sensing net node operation, an intelligent body has an intelligent body sensing net node class at least, and each attribute of intelligent body sensing net node class definition intelligence body is confirmed the attribute multiplicity between each sensing net node thus.Group is the set of a plurality of intelligent bodies that are associated through the role.From another perspective, group is made up of some related roles, and these roles have a plurality of intelligent body instances.When a sensing net node is belonged to a certain group, then this group has just had the attribute of intelligent body on the corresponding sensing net node.
Step 1: the attribute multiplicity between each sensing net node is kept among the adjacency matrix M; Can know according to definition, element in the matrix M
wherein: k representes the number of the same alike result that intelligent body has on i sensing net node and j the sensing net node.
Step 2: according to a sensing net node is to fall into a certain group to use Algebraic Expression; Be defined as
; When falling into a certain group, represent, otherwise represent with 0 with 1.
Step 3: when a desirable sensing net software model guaranteed that model attributes is sound, the attribute multiplicity was minimum between same group more intelligent body, defines following fitness function:
, wherein H refers to total sensing net node number.
, make up sensing net software modeling platform
The first step is set up the domain model of sensing net; Second step generated the graphical definition model of sensing net domain model; The 3rd step generated the instrument definition model of sensing net domain model; The mapping model definition of the 4th step sensing net domain model; The 5th step generated sensing net domain model.
Some embodiment in the face of accompanying drawing of the present invention makes more detailed description down.
According to Fig. 1, the present invention is based upon on the basis of model-driven system and genetic algorithm, and concrete embodiment is:
1, the meta-model figure of the whole domain model of definition
(1) intelligent body class, intelligent body sensing net node class and intelligent body role class
Described intelligent body class definition intelligence body sensing net node class and intelligent body role class; Those can buy and sell the intelligent body instance of resource define division like intelligent body class " buyer " and " seller "; And attribute that instance had of auxiliary definition defines the management characteristic that the supvr has like intelligent body class " supvr ".Described intelligent body class is derived from is intelligent body sensing net node class and intelligent body role class.Described intelligent body sensing net node class definition intelligence body intrinsic sensing net node functional characteristic, as shown in Figure 3, each intelligent body instance that A intelligence body class produces has the information that state variable and communication module can be sent acceptance.Described intelligent body role class definition has the intelligent body of different characteristic along with the time changes, and as shown in Figure 3, identical intelligent body is associated with different intelligent body role class: seller, information broadcast.Seller and information broadcast are exactly intelligent body role, and some intelligent body is played the part of a plurality of roles simultaneously, and these roles are along with the time changes the conversion role.
(2) intelligent body entity and intelligent body class
Described intelligent body entity be one homemade, have surrounding environment reflexive, based on the intelligent body in the intelligent system.As shown in Figure 4, intelligent body a, intelligent body b, intelligent body c are exactly the intelligent body entity of descriptive model system complete or imperfect details.The instance of described intelligent body class comprises intelligent body sensing net node class instance and intelligent body role class instance.As shown in Figure 4, A intelligence body class, B intelligence body class, C intelligence body class and seller, buyer, supvr, saboteur are exactly the instance of intelligent body, and wherein intelligent body b is the entity of a C intelligence body class, and while performer seller and buyer's role.
(3) group, intelligent body group and non intelligent body group
Described group is the set of a plurality of intelligent bodies that are associated through the role, and these are related forms one at the inner figure that is connected of group.Described intelligent body group is exactly that the group of being made up of a plurality of intelligent bodies is used as the bigger group that an intelligent body is formed, and has the attribute of all intelligent bodies.As shown in Figure 5, the group association role, Role Information broadcasting is used by A group and B group, and the buyer role of A group is different with the buyer role's of B group attribute, and these two roles are respectively defined as A role and B role.
(4) intelligent body sensing net node class and intelligent body role assignments is related
The taproot attribute of described intelligent body sensing net node class definition intelligence body, each intelligent body all has at least one intelligent body sensing net node class.Other attributes of intelligence body class definition intelligence body.Intelligence body role class defines intelligent body and plays the part of certain role.The intelligence body is organized inside with role's related being based upon, and intelligent body role assignments is based upon between intelligent body, role and group.As shown in Figure 6, intelligent body b plays the part of the broadcasting role in group B rather than group C, and intelligent body c plays the part of the broadcasting role in group C rather than group B.
Described intelligent body role assignments instance related role, group and an intelligent body; The necessary related role of each intelligent body role assignments and a group; Not necessarily simultaneously related intelligent body; As shown in Figure 6, intelligent body role assignments is related broadcasting and group D do not have related intelligent body instance.
2, the optimizing process of genetic algorithm
(1) genetic coding.Employing is directly perceived and be applicable to that the natural number coding of different scales problem is machine-processed.Each sensor node as gene position, is stipulated that each chromosomal first gene all is the sinking node, by separating of the visit chromosome correspondence problem that is arranged in of order.Each gene position on the chromosome all comprises the relevant information of each sensor node, like node ID, position coordinates, energy etc.Only need constantly the sensor node of gene position value representation to be arranged as required according to the order of sequence during decoding to get final product.Adopt the sensor node sequential coding, the integrality of can guarantee information collecting.
(2) select operator.General heredity selects operator all to produce 1 filial generation by 2 male parents, thereby population scale can diminish gradually.If to male parent's while application 2 kind crossover operator, though can obtain 2 filial generations, the reservation of its pattern is chaotic to l.For guaranteeing population scale and diversity, select the individual male parent of 3n (n is an integer), and the random division scale is 3 n group at every turn, every group intersects with 3n filial generation of generation in twos.With formula
as chromosomal fitness function.Calculate fitness function is minimum in the population 3n individuals as the male parent, after dividing into groups and intersecting in twos, obtain 3n new individual.Simultaneously that fitness function is the poorest 3n existing individuality deleted from population, evolves to more excellent direction to accelerate population.
(3) crossover operator.The order crossover operator is a kind of crossover operator commonly used to the path node coding, can guarantee the legitimacy of offspring individual, can merge the individuality of different orderings simultaneously.If 2 parent individualities are selected point of crossing (with " l " expression) expression as follows at random:
Male parent A:2 5368741;
Male parent B:6 12 781354.
The gene order that then keeps between 2 point of crossing of male parent A is the start-up portion of offspring individual; Be that offspring individual temporarily is (3 68 * * * * *); Remainder generates as follows: begin to judge each gene position from the 2nd point of crossing of male parent B; Appeared in the filial generation like this gene position, then skipped; Otherwise; This gene position is joined in the offspring individual, if arrive the afterbody of male parent B, the section start that then turns back to male parent B continues to add gene by above-mentioned rule to offspring individual; Until generating an offspring individual completely, finally obtain offspring individual and be (3 68 l 542 7).
(4) mutation operator.Select calculating of operator fitness function and crossover operator loaded down with trivial details relatively in the genetic manipulation. under the prerequisite that guarantees algorithm validity; For reducing the operation time of algorithm; Adopt the variation method of 2 gene position in the chiasmatypy at random, variation probability
.
3, make up sensing net software modeling platform
(1) sets up the domain model of sensing net
According to the model that proposes, use the graphical model framework to carry out modeling.At first set up the meta-model of domain model.Next is the project of generation model code and edit code behind the generating code generation model.
(2) the graphical definition model of generation sensing net domain model
Generate Visualization Model graphical definition model, just the view part of visual modeling platform can be revised the icon that defines each entity.
(3) the instrument definition model of generation sensing net domain model
(4) mapping model of sensing net domain model definition
According to domain model, graphical definition model and instrument definition model, generate mapping model.
(5) generation of sensing net domain model
Through translation, generate chart editor generation model.
Claims (1)
1. sensing net software modeling platform development method; When it is characterized in that making up sensing software modeling platform off the net; Introduced the method that the UML model based on intelligent body combines with genetic algorithm; Mode with meta-model defines the domain model based on the UML model of intelligent body, generates the UML model based on intelligent body that is fit to the software development of facing sensing net, and this obtains the better model of performance through genetic algorithm optimization again; Gone out the software modeling platform through graphical model framework developing plug at last, the step that described method comprises is:
Step 1) definition domain model: utilize intelligent body thought and sensing net characteristics; Define domain model figure with the meta-model mode based on the UML of intelligent body; This domain model figure is contained a plurality of illustratons of model; Graph of a relation between comprising role figure that intelligent body figure, picture group that intelligent body belongs to, group that group activity diagram, group are had are had and group and organizing
1.1) intelligent body class, intelligent body sensing net node class and intelligent body role class
Two seed categories are divided and defined to described intelligent body class definition several different methods: intelligent body sensing net node class and intelligent body role class; Intelligence body role class defines the various roles that intelligent body possibly played the part of, and the class definition of intelligent body sensing net node moves the fundamental characteristics of the sensing net node of intelligent body;
1.2) intelligent body entity and intelligent body class
Described intelligent body entity is to be an intelligent body instance based on intelligent system of intelligent body class and the definition of intelligent body by the most basic modeling structure unit; These instances are also directly carried out some important intelligent body instance, the public attribute of the relevant intelligent body of described intelligent body class definition by the bottom property sort of dividing;
1.3) group, intelligent body group and non intelligent body group
Described group is the set of a plurality of intelligent bodies that are associated through the role, and these are related forms one group also is made up of some related roles at the inner figure that is connected of group, and these roles have a plurality of intelligent body instances; Described group preplans a bit; Some is interim urgent foundation; Described intelligent body group is exactly that those itself just have the group of the attribute of intelligent body, has more strong functions, accomplishes more complicated task; Described non intelligent body group is the instance with intelligent bulk properties, is a set rather than intelligent body;
1.4) intelligent body sensing net node class and intelligent body role assignments related
The taproot attribute of the intelligent body that described intelligent body sensing net node class decision is related with it; Intelligence body class determines other attributes of intelligent body; Direct line decision between said intelligent body role class and the intelligent body should be played the part of certain role by the intelligence body; It is promptly inner a specific group that a said intelligent body and role related is based upon in the specific context environmental, and described intelligent body role assignments is entity mapping relations that are based upon between intelligent body, role and the group;
Step 2) optimizes domain model: utilize genetic algorithm that the model that makes up is optimized the model that can obtain perfect performance; The redundancy of attribute between the group in the optimizing process minimizing model; Reduce the complexity of model, make with the group to be that the field modeling of unit is more clear efficient;
Step 3) makes up sensing net software modeling platform: the structure of sensing net software modeling platform; At first set up meta-model domain model based on the UML of intelligent body; Utilize graphical model framework plug-in unit again, set up the domain model of sensing net by the first step; Second step generated the graphical definition model of sensing net domain model; The 3rd step generated the instrument definition model of sensing net domain model; The mapping model definition of the 4th step sensing net domain model; The step of the last model of generation of the 5th step sensing net domain model, the structure of completion platform.
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| CN104978170A (en) * | 2014-04-03 | 2015-10-14 | 中国科学院软件研究所 | Multi-agent system generating method based on graphical expression |
| CN107832044A (en) * | 2017-11-16 | 2018-03-23 | 南京轩世琪源软件科技有限公司 | A kind of common software modeling method based on software meta-model construction |
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| CN104978170A (en) * | 2014-04-03 | 2015-10-14 | 中国科学院软件研究所 | Multi-agent system generating method based on graphical expression |
| CN104978170B (en) * | 2014-04-03 | 2018-07-27 | 中国科学院软件研究所 | A kind of multi-agent system generation method based on graph-based |
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| CN104217100B (en) * | 2014-07-24 | 2017-02-15 | 昆明理工大学 | Modeling simulation method of language competition model based on agent social circle network |
| US10108536B2 (en) | 2014-12-10 | 2018-10-23 | General Electric Company | Integrated automated test case generation for safety-critical software |
| US9940222B2 (en) | 2015-11-20 | 2018-04-10 | General Electric Company | System and method for safety-critical software automated requirements-based test case generation |
| CN107832044A (en) * | 2017-11-16 | 2018-03-23 | 南京轩世琪源软件科技有限公司 | A kind of common software modeling method based on software meta-model construction |
| CN107832043A (en) * | 2017-11-16 | 2018-03-23 | 南京轩世琪源软件科技有限公司 | A kind of visual software modeling method based on software member view construction view |
| CN107832044B (en) * | 2017-11-16 | 2019-02-19 | 安徽修武工业技术有限公司 | A kind of common software modeling method based on software meta-model construction |
| CN107832043B (en) * | 2017-11-16 | 2019-05-03 | 广州今宏信息科技有限公司 | A kind of visual software modeling method based on software member view construction view |
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